The threat of attack on military and civilian targets with biological weapons is a growing national concern and the development of rapid, high-sensitivity, high-accuracy point detection systems for detecting and identifying bioagents in the environment and in biological specimens is a critical requirement. We will focus this effort on a new, broadly applicable method for the detection of B. anthracis spores. Currently available methods for detecting and identifying bioagents provide high sensitivity detection and highly selective identification, but the time required to chemically amplify detectable components and the size/power/cost requirement for sensor readout hardware make current methods unsuitable for point detector systems that can be placed onto any platform (including personnel or unmanned vehicles), dispersed, remotely placed, and networked. We propose to develop a revolutionary method for detecting and identifying bioagents that provides a detailed signature for a biological pathogen in real time without the use of chemical amplification. Our method relies upon measuring the affinity of bioagent outer surface components to a library of ligands immobilized onto a novel biosensor chip. Proof-of-concept of this strategy for detection, identification and discrimination of fungal spores has been demonstrated as part of a NASA funded program for the detection of microorganisms in the environment of manned space vehicles for exploratory space travel. A microarray affinity detection system that relies on both positive affinity of the agent for defined ligands as well as the lack of affinity for a separate set of ligands allows a high degree of redundancy for the specific identification of the agent as well as a powerful technology to avoid "false alarms". The sensor approach utilizes a colorimetric resonant reflectance biosensor that will allow detection hardware to be miniature, low cost, low power, and rugged. Bioagent identification is performed through automated analysis of the recognized outer surface ligands that can classify detected material (spore/bacterium/protein, alive/dead, toxic/nontoxic, pathogen family). The use of a large integrated biosensor array will provide a highly detailed bioagent signature for accurate identification with sufficient sensor redundancy to minimize false alarms. Differential signature response will be demonstrated by exposure of the microarray to vaccine strain B. anthracis spores and spores of other related and unrelated bacteria. The project will focus on developing the ligand library to include non-antibody reagents and differential recognition of bacterial spores from multiple species.